Damage Detection in Beam Structures Based on Frequency-Domain Analysis Methods for Nonlinear Systems by Wenbo Zhang, Xiaoyue Guo, Liangliang Cheng, Bo Zhang

“…To improve the accuracy of the Nonlinear Auto-Regressive with eXogenous inputs (NARX) model and reduce the impact of noise interference, we proposed a Multi-input Multi-output Forward Regression Orthogonal Least Squares (MFROLS) algorithm for processing multi-input multi-output data to identify the NARX model of the same structural system. …”
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STRUCTURAL-COMPOSITIONAL MODEL OF THE NYURBINSKAYA KIMBERLITE PIPE FORMATION (SREDNE-MARKHA AREA OF THE YAKUTIAN DIAMONDIFEROUS PROVINCE) by A. S. Gladkov, D. A. Koshkarev, A. V. Cheremnykh, F. João, M. A. Karpenko, M. V. Marchuk, I. A. Potekhina

“…Analysis of different faults and tectonic fracturing allowed to reconstruct the tectonic stress fields acting at the stage of the kimberlite body formation and to determine their occurrence sequence in time. The data obtained about regularities of the Nyurbinskaya pipe compositional structure and results of geologo-structural studies are combined in a single structural-compositional model of the deposit formation. …”
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Data on behavioural intention to use AI copilot through TAM and AI ecological education policy lensMendeley Data by Emelie C. Villaceran, Celbert M. Himang

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Utility of UAS-LIDAR for estimating forest structural attributes of the Miombo woodlands in Zambia. by Hastings Shamaoma, Paxie W Chirwa, Jules C Zekeng, Abel Ramoelo, Andrew T Hudak, F Handavu, Stephen Syampungani

“…The estimation of forest structural data in area-based forest inventories relies on the relationship between field-based estimates of forest structural attributes (FSA) and lidar-derived metrics at plot level, which can be modeled using either parametric or non-parametric regression techniques. …”
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Advancing plant metabolic research by using large language models to expand databases and extract labeled data by Rachel Knapp, Braidon Johnson, Lucas Busta

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Structural Feature-Preserving Point Cloud Denoising Method for Aero-Engine Profile by Jieqiong Yan, Laishui Zhou, Jun Wang, Xiaoping Wang, Xia Liu

“…In order to ensure that noise is removed without blurring or distorting structural features, a structural feature-preserving point cloud denoising method is proposed. …”
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Structural Component Identification and Damage Localization of Civil Infrastructure Using Semantic Segmentation by Piotr Tauzowski, Mariusz Ostrowski, Dominik Bogucki, Piotr Jarosik, Bartłomiej Błachowski

“…The data includes semantic segmentation masks for both categorizing structural elements (slabs, beams, and columns) and assessing structural damage (concrete spalling or exposed rebars). …”
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Deep learning of structural morphology imaged by scanning X-ray diffraction microscopy by Aileen Luo, Tao Zhou, Martin V. Holt, Andrej Singer, Mathew J. Cherukara

“…Abstract Scanning X-ray nanodiffraction microscopy is a powerful technique for spatially resolving nanoscale structural morphologies by diffraction contrast. One of the critical challenges in experimental nanodiffraction data analysis is posed by the convergence angle of nanoscale focusing optics which creates simultaneous dependency of the far-field scattering data on three independent components of the local strain tensor—corresponding to dilation and two potential rigid body rotations of the unit cell. …”
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Interpretive Structural Modeling of Factors Affecting Organizational Ambidexterity in the Ministry of Sports and Youth by Ainolah Ahmadnejad Joghie, Mozafar Yektayar, Mozhgan Khodamoradpoor

“…The statistical population of this study consisted of experts who were selected purposefully based on theoretical saturation of 15 of them. Semi-structured interviews were used to collect data in the qualitative study and a questionnaire was used to collect data in the quantitative study. …”
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Intelligent Identification of Structural Damage Based on the Curvature Mode and Wavelet Analysis Theory by Longsheng Bao, Yue Cao, Xiaowei Zhang

“…Data fitting is then performed to estimate the degree of structural damage. …”
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Predictive modeling of visible-light azo-photoswitches’ properties using structural features by Said Byadi, P. K. Hashim, Pavel Sidorov

“…Abstract In this manuscript we present the strategy for modeling photoswitch properties (maximum absorption wavelength and thermal half-life of photoisomers) of visible-light azo-photoswitches using structural data. We compile a comprehensive data set from literature sources and perform a rigorous benchmark to select the best feature type and modeling approach. …”
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Structural Constraints in Current Stomatal Conductance Models Preclude Accurate Prediction of Evapotranspiration by Pushpendra Raghav, Mukesh Kumar, Yanlan Liu

“…In contrast, a ML approach, wherein the model structure is learned from the data, outperforms traditional models, thus highlighting that there still is significant room for improvement in the structure of traditional models for predicting ET. …”
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Enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effects by A. Presno Vélez, M. Z. Fernández Muñiz, J. L. Fernández Martínez

“…This research aimed to develop a methodology for training an artificial intelligence (AI) system to predict the effects of retrofitting on civil structures, using data from the KW51 bridge (Leuven). …”
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THE METHOD OF ADAPTIVE STATISTICAL CODING TAKING INTO ACCOUNT THE STRUCTURAL FEATURES OF VIDEO IMAGES by Volodymyr Barannik, Dmytro Havrylov, Serhii Pantas, Yurii Tsimura, Tatayna Belikova, Rimma Viedienieva, Vasyl Kryshtal

“…To reduce the output volume, the RLE algorithm and integral arithmetic coding are adapted to the new structure of the input data. Thus, the method of linearization of two-dimensional transformants based on zig-zag scanning was further developed. …”
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Synthesis and structural study of the Linde Type-A zeolite prepared from kaolinite by Szilvia Ormándi, István Dódony

“…The measured hkl and intensity data sets are the inputs for structure determination. …”
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Spearman Rank Correlation PCA for Mixed Scale Indicator in Structural Equation Modeling by Lisa Asaliontin, Eni Sumarminingsih, Solimun Solimun, Mohammad Ohid Ullah

“…Structural Equation Modeling (SEM) is a statistical modeling technique that integrates measurement models and structural models simultaneously. …”
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Durability Test and Service Life Prediction Methods for Silicone Structural Glazing Sealant by Bo Yang, Junjin Liu, Jianhui Li, Chao Wang, Zhiyuan Wang

“…Silicone structural glazing (SSG) sealants are crucial sealing materials in modern building curtain walls, whose performance degradation may lead to functional and safety issues, posing significant challenges to building safety maintenance. …”
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Co-methylation networks associated with cognition and structural brain development during adolescence by Dawn Jensen, Dawn Jensen, Jiayu Chen, Jiayu Chen, Jessica A. Turner, Julia M. Stephen, Yu-Ping Wang, Tony W. Wilson, Vince D. Calhoun, Vince D. Calhoun, Vince D. Calhoun, Vince D. Calhoun, Vince D. Calhoun, Jingyu Liu, Jingyu Liu, Jingyu Liu

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STRUCTURAL EQUATION MODELING MULTIGROUP INDIRECT EFFECTS ON BANK MORTGAGE PAYMENT TIMELINESS by Ulfah Maisaroh, Adji Achmad Rinaldo Fernandes, Atiek Iriany

“…Structural Equation Modeling (SEM) is a multivariate statistical method that is used to thoroughly explain the relationship between latent variables simultaneously. …”
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An Agglomerative Clustering Combined with an Unsupervised Feature Selection Approach for Structural Health Monitoring by Tales Boratto, Heder Soares Bernardino, Alex Borges Vieira, Tiago Silveira Gontijo, Matteo Bodini, Dmitriy A. Martyushev, Camila Martins Saporetti, Alexandre Cury, Flávio Barbosa, Leonardo Goliatt

“…Structural health monitoring (SHM) is critical for ensuring the safety and longevity of structures, yet existing methodologies often face challenges such as high data dimensionality, lack of interpretability, and reliance on extensive label datasets. …”
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